1. Field of the Invention
This invention relates generally to a method and system for developing fault models and, more particularly, to a method and system for developing fault models from structured text document sources, such as service procedures, which extracts symptoms, failure modes, serviceable parts, and correlations among them from diagnostic fault information in the document, parses testing procedures to identify more fault model data, uses reachability analysis to find hidden dependencies, and assembles all of the extracted data into a resultant fault model.
2. Discussion of the Related Art
Modern vehicles are complex electro-mechanical systems that employ many sub-systems, components, devices, and modules, which pass operating information between and among each other using sophisticated algorithms and data buses. As with anything, these types of devices and algorithms are susceptible to errors, failures and faults that can affect the operation of the vehicle. To help manage this complexity, vehicle manufacturers develop fault models, which match the various failure modes with the symptoms exhibited by the vehicle.
Vehicle manufacturers commonly develop fault models from a variety of different data sources. These data sources include engineering data, service procedure documents, text verbatim from customers and repair technicians, warranty data, and others. While all of these fault models can be useful tools for diagnosing and repairing problems, the development of the fault models can be time-consuming, labor intensive, and in some cases somewhat subjective. In addition, manually-created fault models may not consistently capture all of the failures modes, symptoms, and correlations which exist in the vehicle systems. Furthermore, a wealth of fault model data resides in legacy service documents, where it is often only partially extracted, or is overlooked altogether because of the difficult and error-prone nature of manually translating text into failure modes, symptoms, serviceable parts, and correlation data.
There is a need for a method for developing fault models from different types of structured textual data sources. Such a method could not only reduce the amount of time and effort required to create fault models, but could also produce fault models with more and better content, thus leading to more accurate failure mode diagnoses in the field, reduced repair time and cost, improved first time fix rate, and improved customer satisfaction.